{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color=\"maroon\"><h4 align=\"center\">Pandas Group By</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In this tutorial we are going to look at weather data from various cities and see how group by can be used to run some analytics.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>85</td>\n",
       "      <td>12</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>87</td>\n",
       "      <td>15</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day      city  temperature  windspeed   event\n",
       "0   1/1/2017  new york           32          6    Rain\n",
       "1   1/2/2017  new york           36          7   Sunny\n",
       "2   1/3/2017  new york           28         12    Snow\n",
       "3   1/4/2017  new york           33          7   Sunny\n",
       "4   1/1/2017    mumbai           90          5   Sunny\n",
       "5   1/2/2017    mumbai           85         12     Fog\n",
       "6   1/3/2017    mumbai           87         15     Fog\n",
       "7   1/4/2017    mumbai           92          5    Rain\n",
       "8   1/1/2017     paris           45         20   Sunny\n",
       "9   1/2/2017     paris           50         13  Cloudy\n",
       "10  1/3/2017     paris           54          8  Cloudy\n",
       "11  1/4/2017     paris           42         10  Cloudy"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"weather_by_cities.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For this dataset, get following answers,\n",
    "#### 1. What was the maximum temperature in each of these 3 cities?\n",
    "#### 2. What was the average windspeed in each of these 3 cities?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x000001732ADB2550>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = df.groupby(\"city\")\n",
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DataFrameGroupBy object looks something like below,**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"group_by_cities.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city: mumbai\n",
      "\n",
      "\n",
      "data:         day    city  temperature  windspeed  event\n",
      "4  1/1/2017  mumbai           90          5  Sunny\n",
      "5  1/2/2017  mumbai           85         12    Fog\n",
      "6  1/3/2017  mumbai           87         15    Fog\n",
      "7  1/4/2017  mumbai           92          5   Rain\n",
      "city: new york\n",
      "\n",
      "\n",
      "data:         day      city  temperature  windspeed  event\n",
      "0  1/1/2017  new york           32          6   Rain\n",
      "1  1/2/2017  new york           36          7  Sunny\n",
      "2  1/3/2017  new york           28         12   Snow\n",
      "3  1/4/2017  new york           33          7  Sunny\n",
      "city: paris\n",
      "\n",
      "\n",
      "data:          day   city  temperature  windspeed   event\n",
      "8   1/1/2017  paris           45         20   Sunny\n",
      "9   1/2/2017  paris           50         13  Cloudy\n",
      "10  1/3/2017  paris           54          8  Cloudy\n",
      "11  1/4/2017  paris           42         10  Cloudy\n"
     ]
    }
   ],
   "source": [
    "for city, data in g:\n",
    "    print(\"city:\",city)\n",
    "    print(\"\\n\")\n",
    "    print(\"data:\",data)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This is similar to SQL,**\n",
    "\n",
    "**SELECT * from weather_data GROUP BY city**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>85</td>\n",
       "      <td>12</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>87</td>\n",
       "      <td>15</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day    city  temperature  windspeed  event\n",
       "4  1/1/2017  mumbai           90          5  Sunny\n",
       "5  1/2/2017  mumbai           85         12    Fog\n",
       "6  1/3/2017  mumbai           87         15    Fog\n",
       "7  1/4/2017  mumbai           92          5   Rain"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.get_group('mumbai')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>92</td>\n",
       "      <td>15</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paris</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               day  temperature  windspeed  event\n",
       "city                                             \n",
       "mumbai    1/4/2017           92         15  Sunny\n",
       "new york  1/4/2017           36         12  Sunny\n",
       "paris     1/4/2017           54         20  Sunny"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>88.50</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>32.25</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paris</th>\n",
       "      <td>47.75</td>\n",
       "      <td>12.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          temperature  windspeed\n",
       "city                            \n",
       "mumbai          88.50       9.25\n",
       "new york        32.25       8.00\n",
       "paris           47.75      12.75"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This method of splitting your dataset in smaller groups and then applying an operation \n",
    "(such as min or max) to get aggregate result is called Split-Apply-Combine. It is illustrated in a diagram below**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"split_apply_combine.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>85</td>\n",
       "      <td>5</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paris</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               day  temperature  windspeed   event\n",
       "city                                              \n",
       "mumbai    1/1/2017           85          5     Fog\n",
       "new york  1/1/2017           28          6    Rain\n",
       "paris     1/1/2017           42          8  Cloudy"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">mumbai</th>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>88.500000</td>\n",
       "      <td>9.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.109126</td>\n",
       "      <td>5.057997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>86.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>88.500000</td>\n",
       "      <td>8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90.500000</td>\n",
       "      <td>12.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">new york</th>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32.250000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.304038</td>\n",
       "      <td>2.708013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>6.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>32.500000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.750000</td>\n",
       "      <td>8.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">paris</th>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47.750000</td>\n",
       "      <td>12.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.315073</td>\n",
       "      <td>5.251984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.250000</td>\n",
       "      <td>9.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>11.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>14.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                temperature  windspeed\n",
       "city                                  \n",
       "mumbai   count     4.000000   4.000000\n",
       "         mean     88.500000   9.250000\n",
       "         std       3.109126   5.057997\n",
       "         min      85.000000   5.000000\n",
       "         25%      86.500000   5.000000\n",
       "         50%      88.500000   8.500000\n",
       "         75%      90.500000  12.750000\n",
       "         max      92.000000  15.000000\n",
       "new york count     4.000000   4.000000\n",
       "         mean     32.250000   8.000000\n",
       "         std       3.304038   2.708013\n",
       "         min      28.000000   6.000000\n",
       "         25%      31.000000   6.750000\n",
       "         50%      32.500000   7.000000\n",
       "         75%      33.750000   8.250000\n",
       "         max      36.000000  12.000000\n",
       "paris    count     4.000000   4.000000\n",
       "         mean     47.750000  12.750000\n",
       "         std       5.315073   5.251984\n",
       "         min      42.000000   8.000000\n",
       "         25%      44.250000   9.500000\n",
       "         50%      47.500000  11.500000\n",
       "         75%      51.000000  14.750000\n",
       "         max      54.000000  20.000000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "mumbai      4\n",
       "new york    4\n",
       "paris       4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paris</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          day  temperature  windspeed  event\n",
       "city                                        \n",
       "mumbai      4            4          4      4\n",
       "new york    4            4          4      4\n",
       "paris       4            4          4      4"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "mumbai      Axes(0.125,0.125;0.775x0.775)\n",
       "new york    Axes(0.125,0.125;0.775x0.775)\n",
       "paris       Axes(0.125,0.125;0.775x0.775)\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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S9nMtUVLHU1lZSVlZGf4+antmzpzJrFmzeOedd+jfv3++m9Nq9nVM1r4OlMUY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\nvr3UydpxlvdLGCGEQ4DbgY/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1732adeec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Tp8KXX3p/yFes8MZMGDQIli1T50XxqO9AdCk5EJGoqF3bez30tm0wd673/ob+/b1Xsael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\nO9zRo0cLfL80MTn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iIj5KDkRERMRHyYGISJjtP7yfD3Z/wMmsk9EORSQkVaIdgIhIeZfl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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1732adee8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OPPNMEokEc+bMqXZcpfvKz89n4MCB/PSnP2X79u1V+m3evBlIXo6YOHEi9913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\nSZJSMixIkqSU/n9haJf99gnyCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1732adee240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "g.plot()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
